Photovoltaic Cell Parameter Identification And Photovoltaic Module Fault Diagnosis Based On LFCOOT Algorithm

Solar energy is regarded as one of the most potential energy sources in the 21 st century.Photovoltaic power generation,as the core of solar energy field,has been developing rapidly in recent years.The internal parameters and output power of PV modules in actual operation are affected by environmental factors such as irradiance and temperature.The accurate modeling of PV cell model and PV module model is one of the most important and urgent problems to be solved in PV system.In addition,photovoltaic modules will inevitably have various faults during operation,so how to diagnose the fault type of photovoltaic modules quickly and accurately is also crucial.The main work of this paper is as follows:(1)The photovoltaic cell model and photovoltaic module model are established,and the output characteristic curves of photovoltaic cells and photovoltaic modules are obtained by simulation.The influence of irradiance and temperature on the output characteristic curves of photovoltaic cells and the influence of four common faults on the output characteristics of photovoltaic modules are analyzed.(2)Accurate identification of model parameters of photovoltaic cells and photovoltaic modules is one of the effective methods to establish accurate models of photovoltaic cells and photovoltaic modules.In this paper,an improved COOT optimization algorithm based on Levy flight strategy(LFCOOT)is proposed.In view of the shortcomings of COOT optimization algorithm,such as complex calculation,slow convergence speed and insufficient accuracy,the algorithm was improved as follows: <1> The Sampling current is used to replace the calculated current in the exponential term of the photovoltaic cell output current mathematical model to improve the prediction accuracy and simplify the calculation;<2> To improve the phenomenon that leader invariance in the traditional COOT algorithm is prone to cause local optimization,the real-time update leader strategy is adopted to improve the global search ability and convergence speed of the algorithm;<3> Levy flight strategy is introduced to increase algorithm accuracy.The measured data of three widely used photovoltaic cells and photovoltaic modules are identified.The root-mean-square error is used as the objective function to evaluate the accuracy of the algorithm,and the relative error is introduced to evaluate the deviation between the identification results and the actual data.Compared with fifteen algorithms such as traditional COOT algorithm,enhanced Harris Eagle optimization algorithm,performance-oriented JAYA algorithm improved chaos optimization algorithm and generalized optimization based teaching and learning algorithm,the experimental results show that the proposed LFCOOT algorithm has high accuracy,fast convergence speed and small relative error value.In addition,the parameters of three commercial PV modules are identified,and the fitted output characteristic curve error is small,which verifies the reliability of the algorithm in practical application.(3)Rapid diagnosis of photovoltaic module fault types is one of the effective methods to improve system stability.In recent years,many scholars have applied machine learning algorithm to photovoltaic module fault diagnosis.In this paper,LFCOOT algorithm is used to optimize two important parameters in Support Vector Machine(SVM): <1> The inner diameter of kernel function is optimized to improve the classification accuracy;<2> The punishment factor was optimized to improve the sample fitting ability and classification ability after testing.The improved classification model is named LFCOOT algorithm optimization support vector machine classification model(LFCOOT-SVM).The UCI data set is used to evaluate the classification model and compare it with other classification models,such as improved multiverse algorithm optimization support vector machine and Bat algorithm optimization support vector machine,which verified the superiority of the proposed LFCOOT-SVM classification model.The improved LFCOOT-SVM has better performance.Finally,according to the fault characteristics of photovoltaic modules,a fault diagnosis method combining the internal parameters of photovoltaic modules is proposed.The operating data of photovoltaic modules in different fault states are identified and used as sample data for photovoltaic module fault diagnosis.In this paper,the model parameters of photovoltaic modules based on single diode model are identified and 500 groups of data are recorded as data sets for fault diagnosis research.Compared with the fault diagnosis method based on the I-V output characteristic curve of photovoltaic modules,the results show that the fault diagnosis method based on parameter identification can reflect the fault type more accurately,indicating that the application of parameter identification to the fault diagnosis of photovoltaic modules is feasible and effective.